Departamento de Informática
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Browsing Departamento de Informática by Author "Abreu, António José Marques"
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- LudVision Remote Detection of Exotic Invasive Aquatic Floral Species using Data from a DroneMounted Multispectral SensorPublication . Abreu, António José Marques; Alexandre, Luis Filipe Barbosa de Almeida; Santos, João AmaralRemote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring it’s reflected and emitted radiation at a distance. It is being broadly used to monitor ecosystems, mainly for their preservation. There have been evergrowing reports of invasive species affecting the natural balance of ecosystems. Exotic invasive species have a critical impact when introduced into new ecosystems and may lead to the extinction of native species. In this study, we focus on Ludwigia peploides, considered by the European Union as an aquatic invasive species. Its presence can have negative impacts on the surrounding ecosystem and human activities such as agriculture, fishing, and navigation. Our goal was to develop a method to identify the presence of the species. To achieve this, we used images collected by a dronemounted multispectral sensor. Due to the lack of publicly available data sets containing Ludwigia peploides, we had to create our own data set. We started by carefully studying all the available options. We first experimented with satellite images, but it was impossible to identify the targeted species due to their low resolution. Thus, we decided to use a dronemounted multispectral sensor. Unfortunately, due to budget limitations, we could not acquire the highly specialized types of equipment that is more commonly used in remote sensing. However, we were confident that our setup would be enough to extract the species’ spectral signature, and that the higher resolution compared to satellites would allow us to use deep learning models to identify the species. The use of the drone allowed for better operational flexibility and to cover a large area. The multispectral sensor allowed us to leverage the information of two additional bands outside the visible spectrum. After visiting the study site multiple times and capturing data at various times of the day, we created a representative data set with different atmospheric conditions. After the data collection, we proceeded to the preprocessing and annotation steps to have a usable data set. In later stages, we proved that extracting the specie’s spectral signature from our data set is possible. This was a significant conclusion, as it proved that it is indeed possible to differentiate the species’ spectral signature with equipment that is not as advanced and specialized as the ones used in other studies. After having a data set, we focused on the next step, which was to develop and validate a method that would be able to identify Ludwigia p on our data. We decided on using semantic segmentation models to identify the species. Given that we only have two additional bands compared to traditional RGB images, we could not approach the problem as a standard remote sensing spectroscopy problem. By using semantic segmentation models, we can leverage both the capabilities of these models to recognize objects and the multispectral nature of our data. Fundamentally, the model has the same behavior as usual but has access to the information of two additional bands.We started by using an existing stateoftheart semantic segmentation model adapted to handle our data. After doing some initial tests and establishing a baseline, we proposed and implemented some changes to the existing model. The goal of the modifications was to create a model with lower training times and better performance in detecting Ludwigia p. at high altitudes. The result is a new model better suited to our data and application. Our model is faster when it comes to training time while maintaining similar performance and has a slight performance increase in highaltitude images.